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Semantic differentiation in speech emotion recognition: insights from descriptive and expressive speech roles

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DOIResolve DOI: https://doi.org/10.18653/v1/2025.starsem-1.6
AuthorSearch for: 1; Search for: 1; Search for: 2, 1ORCID identifier: https://orcid.org/0000-0001-6241-6114; Search for: 1; Search for: 1
Affiliation
  1. University of Ottawa
  2. National Research Council Canada. Digital Technologies
FormatText, Article
Conference14th Joint Conference on Lexical and Computational Semantics, SEM 2025, November 2025, Suzhou, China
Abstract
Speech Emotion Recognition (SER) is essential for improving human-computer interaction, yet its accuracy remains constrained by the complexity of emotional nuances in speech. In this study, we distinguish between descriptive\ semantics, which represents the contextual content of speech, and expressive\ semantics, which reflects the speaker’s emotional state. After watching emotionally charged movie segments, we recorded audio clips of participants describing their experiences, along with the intended emotion tags for each clip, participants’ self-rated emotional responses, and their valence/arousal scores. Through experiments we show that descriptive semantics align with intended emotions, while expressive semantics correlate with evoked emotions. Our findings inform SER applications in human-AI interaction and pave the way for more context-aware AI systems.
Publication date
PublisherAssociation for Computational Linguistics
PlaceStroudsburg, Pennsylvania, United States
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In
LanguageEnglish
Peer reviewedYes
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Record identifier112550c0-2fc3-489d-835c-4f09e5c9cd65
Record created2025-11-28
Record modified2026-02-20

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